Optimasi Routing pada Metropolitan Mesh Network Menggunakan Adaptive Mutation Genetic Algorithm

  • Merinda Lestandy Universitas Brawijaya
  • Sholeh Hadi Pramono Universitas Brawijaya
  • Muhammad Aswin Universitas Brawijaya
Keywords: Metropolitan Mesh Network (MMN), Optimasi Routing, Adaptive Mutation Genetic Algorithm (AMGA)

Abstract

In dynamic and wide networks, such as Metropolitan Mesh Network (MMN), routing becomes very complex because a packet can be blocked before it reaches its destination. In addition, users can also log in or log out from network topology. Therefore, a good routing algorithm, which is able to reduce time in network update process or when there is an error in the network, are required. Routing problems can be represented as the shortest path problem to facilitate completion. In this paper, a routing algorithm optimization using Adaptive Mutation Genetic Algorithm (AMGA) on MMN is presented by determining a probability of 0.000005782 at the beginning, with crossover probability of 0.000847, to reduce or avoid premature convergence.

References

Kessler, Gary C. & Train, David A. (1999). Metropolitan Area Networks: Concepts, Standards, and Services. Network : McGraw-Hill.

Miniwatss Marketing Group. (2017). http://www.internetworldstats.com/stats.htm, 12 Maret 2017, pk. 15.19.

Kumar, R. & Kumar , M. (2010). Exploring Genetic Algorithm for Shortest Path Optimization in Data Networks. Global Journal of Computer Science and Technology. Vol. 10 Issues 11 (Ver. 1.0), p 8-12.

Lin, X. H., Kwok Y. K., & Lau V. K. N. (2002). A genetic algorithm based approach to route selection and capacity flow assignment. Computer Communications. p 96-974.

Yun, Y. (2006). Hybrid genetic algorithm with adaptive local search scheme. Computer & Industrial Engineering 51:p.821-838.

Korejo, Imtiza Ali. (2010). Adaptive mutation Operators for Evolutionary Algorithms. Department of Computer Science for the degree of Doctor of Philosophy.

Yoon, Chang-Pyo & Ryou, Hwang-Bin. (2011). A Genetic Algorithm for the Routing Protocol of Wireless Mesh Networks. Information Science and Applicatiobs (ICISA), 2011 International Conference on Jeju Island, South Korea. IEEE. p.1-6.

Koyama, Akio, Toshiki Nishie, Jupei Arai, & Leonard Barolli. (2005). A New Quality of Service Multicast Routing Protocol Based on Genetic Algorithm. Procedings of the 2005 11th International Conference on Parallel and Distributed System (ICPADS’055). IEEE. p.1-6.

Seetaram, J., & Kumar, P Satish. (2016). An Energy Aware Genetic Algorithm Multipath Distance Vector Protocol for Efficient Routing. Wireless Communications, Signal Processing and Networking (WiSPNET), International Conference on Chennai, India. IEEE. p.1975-1980.

Guo, Lejiang & Tang, Qiang. (2010). An Improved Routing Protocol in WSN with Hybrid Genetic Algorithm. Second International Conference on Networks Security, Wireless Communications and Trusted Computing, IEEE. p.289-292.

Apetroaei, Ioana., Ionut-Alexandru Oprea, Bogdan-Eugen Proca, & Laura Gheorghe. (2011). Genetic algorithms applies in routing protocols for wireless sensor networks. Roedunet International Conference (RoEduNet). IEEE.

Rajakumar, B. R., & George, Dr. Aloysius. (2012). A New Adaptive Mutation Technique for Genetic Algorithm. IEEE International Conference on Computational Intelligence and Computing Research.

Published
2017-11-29
How to Cite
Merinda Lestandy, Sholeh Hadi Pramono, & Muhammad Aswin. (2017). Optimasi Routing pada Metropolitan Mesh Network Menggunakan Adaptive Mutation Genetic Algorithm. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 6(4), 430-435. Retrieved from https://jurnal.ugm.ac.id/v3/JNTETI/article/view/2811
Section
Articles